Accession Number:



Application of Kernel Density Estimation to Achieve Automated Near Real-Time Antenna Pattern Data Processing and Analysis in an Anechoic Chamber

Descriptive Note:

[Technical Report, Technical Paper]

Corporate Author:

412 TW EWG 772 Test Squadron

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Report Date:


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The Benefield Anechoic Facility BAF at Edwards Air Force Base is the worlds largest known anechoic chamber. Due to its unmatched size and complement of test equipment, the BAF hosts far-field pattern measurements at all azimuth angles and multiple simultaneous elevations of installed antennas on large aircraft across a frequency range of 0.1 18 GHz. Antenna tests at the BAF rapidly produce large quantities of data, which often require immediate analyses to allow system owners to make relevant improvements. Historically, the BAF had accomplished quality assurance manually. Today, the BAF team has developed scripts that use kernel density estimation and basic machine learning to automatically check incoming data for errors and highlight unusual results for review. During a 2019 test of over sixty installed antennas on a B-1B bomber, the BAF team used these scripts to produce calibrated, quality-assured antenna patterns in near real-time. Rapid processing brings deficiencies to the customers attention fast enough to allow corrections to be applied and re-tested during the same test event highly significant and valuable as aircraft and BAF schedule times are limited and may be a one-time opportunity to gather required data. This paper explores the algorithm used to evaluate antenna patterns, as well as the expected characteristics of patterns that enable the selection of relevant data. Development and application of this algorithm found that using kernel density estimation to calculate the number of maxima in a patterns distribution of gain values, then performing this recursively over only the main lobe, can identify problems such as incorrect switching, mismatched transmission lines, and multipath. Algorithm optimization was achieved using generated data, then verified by applying the algorithm to previous test data.

Subject Categories:

  • Cybernetics
  • Electrical and Electronic Equipment

Distribution Statement:

[A, Approved For Public Release]